DEPARTMENT OF ECONOMICS

THE ROLE OF POLITICS AND INSTITUTIONS

IN LDC CURRENCY DEVALUATIONS

Anja Shortland, University of Leicester, UK

Working Paper No. 04/30 December 2004

1

THE ROLE OF POLITICS AND INSTITUTIONS

IN LDC CURRENCY DEVALUATIONS⊕

Anja K Shortland

Abstract

This paper examines political, institutional and economic determinants of exchange

rate performance in less developed countries in the 1990s. It models exchange rate

depreciations as two separate processes, firstly a process determining whether a

currency is devalued and secondly a process determining the size of devaluation. The

paper utilizes the most recent political and institutional data as well as a new index of

central bank governor turnover in the 1990s to examine the relative importance of

political and economic factors. While institutional and political factors dominate the

probability of devaluation, the size of devaluations is mainly governed by economic

factors.

Keywords: exchange rate systems, less developed countries, speculative attack,

fundamentals, institutions

⊕ The author would like to thank Arnie Aassve, Panicos Demetriades, David Fielding, Jose Noguera, David Stasavage, Andrew Walter and seminar participants at the University of Leicester for helpful comments and generosity with their time. All remaining errors are mine.

I: Introduction

Academics’ and policy-makers’ interest in developing country exchange rate regimes

and their performance received a major boost in the 1990s. Firstly the decade saw the

emergence of a large number of new (and newly convertible) currencies following the

break-up of the Soviet Union. Choosing an exchange rate regime, which could deliver

monetary stability was an important aspect of successfully managing economic

transition. Several governments opted for currency pegs to affect inflation expectations

and “borrow” credibility. A second major trend in the 1990s was financial liberalization

in less developed countries (LDCs). This resulted in increasing financial integration and

capital mobility, but also in increasing financial fragility: The 1990s saw several periods

of turmoil on the foreign exchange markets, when central banks were faced with such

massive speculative attacks that many currency pegs had to be abandoned.

Academic research on exchange rate regimes reflects that exchange rates are determined

both by government preferences and market pressures. The literature on regime choice

examines the economic, political and institutional factors that predispose a country

towards choosing a floating exchange rate, a soft peg, a hard peg or monetary union.1

The relevant institutions and economic factors are drawn from the literature on optimal

currency areas2, the “fear of floating” hypothesis3 and political economy arguments.4

The literature on currency crises on the other hand looks at the interaction between

governments and markets. It initially focused on inconsistencies between the announced

peg regime and the monetary and fiscal policies implemented by the country.5 More

recently the currency crises literature has also incorporated political6 and financial7

institutions, which determine the cost of peg defence, and the way in which politicians

discount future benefits from maintaining a peg versus the short-term costs of defending

the currency against a speculative attack.8 This paper contributes to both these

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literatures by examining the factors that determine whether a country maintains peg

stability in a given year and if there is a devaluation, what factors determine the size of

the devaluation.

The first contribution of the paper is the statistical examination of the most recent data

on politics and institutions in a panel of LDCs, which are concurrent with the emerging

market currency crises of the 1990s. For the question of whether peg stability is

maintained the focus is on the credibility of the commitment to the peg. A very

important explanatory variable is an index of central bank governor turnover in the

1990s. This was constructed for this study to avoid using 1980s data, which are both

limited to non-transition countries and are in many cases out of date, given moves in

many countries to improve central bank independence. While the question of political

stability has been the focus of a number of previous studies on speculative attacks9 and

regime choice10, the question of central bank independence has been neglected or been

examined with 1980s data11. However, if the central bank is charged with maintaining

peg stability and stands above the political fray (as for example in Estonia), even high

political instability may not feed into devaluation expectations.

The second contribution of the paper is that unlike the regime choice literature it does

not use multinomial logit analysis (somewhat arbitrarily) distinguishing between

“intermediate” and “freely floating” regimes. Instead a political economy model of the

size of devaluations is estimated. Devaluation size should reflect economic factors,

however, the credibility of the government’s commitment to maintaining monetary and

fiscal discipline may also influence the size of the devaluation.

The third contribution arises from disentangling the decision to devalue from the size of

devaluation. This helps to give a more nuanced picture of institutional factors, which

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may have a negative effect on the probability of devaluation, but which can have a

positive effect on the size of devaluation if it occurs. For example a democratic

government facing an election is likely to prioritise internal over external objectives and

therefore unlikely to impose the cost of pegging on the electorate. However, it is also

unlikely to permit a catastrophic devaluation, as it would fear being punished for

economic mismanagement. Such ambiguous effects of the institutional environment on

exchange rate stability could not be picked up by previous studies of the effect of

politics on currency pressure using linear regression analyses.12

The study is based on a panel of less developed countries from 1990 to 2000. There are

a number of reasons for looking at developing countries separately from developed

countries. Currency pegs in LDCs are generally unilateral. The stability of LDC

exchange rate pegs therefore relies on the countries’ economic performance and the

credibility of their governments. In the absence of timely and reliable economic data,

investors and speculators are likely to focus on the preferences and commitment

mechanisms entered into by LDC governments when predicting the stability of pegs.

Political and institutional factors should therefore take on a special significance in the

LDC context.

It is shown that institutional factors dominate whether or not a devaluation occurs, even

when economic control variables are included in the regression. Central to peg

maintenance is the credibility of the commitment to exchange rate stability. This

credibility is primarily a function of the degree of independence of the central bank, but

also of the government’s position in the polity and its time horizon and the level of

financial development. Another significant factor is economic size, as argued by the

literature on regime choice. If a government does not maintain a peg, however, the size

of the devaluation appears to be mainly determined by the degree of internal

4

imbalances, the need to restore competitiveness and the degree of capital mobility, with

some scope for using foreign exchange reserves to limit the size of devaluations. The

empirical evidence for the importance of political factors in determining the size of

devaluation is less strong, but an interesting result of modelling the devaluation problem

as a two separate processes is that some variables change sign. While democracies are

less likely to maintain a peg, they are also more likely to have smaller devaluations.

Autocracies, which can postpone devaluations because they are able to impose the costs

of peg defence on the population, tend to have higher devaluations when they occur.

Financial sector development lowers the probability of devaluation, but if a more

financially developed country is forced into a devaluation, it is likely to experience a

major crisis.

The paper is organized as follows. In section 2 the relevant economic and institutional

variables governing the probability and size of devaluations are identified through a

review of the literatures on regime choice and currency crises and some descriptive

statistics are presented. Section 3 discusses the methodology for this paper. Section 4

presents the results on the factors determining the probability of devaluation and the

size of devaluations and discusses the results. Section 5 concludes.

II: Data Section

1 Dependent variable:

The dependent variable in the analysis is the change in the log of the annual average

exchange rate vis-à-vis US$, unless a peg to another hard currency (Euro [DM, FF],

SDR etc) is explicitly declared.13 For the bivariate analysis the variable is categorized

with all revaluations and peg stability as defined by the IMF (fluctuations within a 2%

band) making up the “no devaluation” category and devaluations greater than 2%

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making up the “devaluation” category. Overall about a third of the observations are in

the “no devaluation” category. In the regression analysis studying the size of

devaluation, the dependent variable is the change in the log of the annual average

exchange rate in those countries, in which the devaluation of the average annual

exchange rate exceeds 2%.14

2 Institutional Factors

According to the second-generation currency crises literature the main determinant of

whether or not a currency crisis occurs is whether the authorities are willing to bear the

political costs of defending the currency. The size of devaluation on the other hand

depends on the change in monetary policy once the peg is abandoned, i.e. whether and

to what degree the authorities will relax monetary policy once the constraints imposed

by the peg are eased. Peg defence in a developing country context with limited foreign

exchange reserves and without multilateral support involves raising the interest rate to

stem capital outflows and (over time) correct any loss of competitiveness caused by past

inflation differentials. There are several reasons why the authorities may resist such a

rise in interest rates.

Time Horizons: According to macro-economic feedback models, the government

resists interest rate rises because of their effects on the real economy, causing

unemployment, bankruptcies and hence slower growth.15 The authorities’ willingness to

bear these costs depends mainly on the time horizon of the policy-maker, who weighs

up the (short-term) costs of peg defence against the (longer-term) benefits of exchange

rate stability. One option of demonstrating commitment to a peg is to delegate peg

maintenance to an independent central bank set up to maximize welfare over a longer

time horizon than politicians.16 The time horizon of politicians in turn depends on the

stability of the polity: whether and when they have to face an election and how likely it

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is that they will lose power.17 In this study the question of time horizons will be

captured with the following proxies:

Central Bank Governor Turnover: it was impossible to find a comprehensive dataset

on the degree of central bank independence for developing countries in the 1990s. The

Cukierman [1992] dataset of the de jure independence of central banks is a

geographically limited data-set for the 1980s, although it has been updated with a

number of entries for post-Soviet countries by Cukierman et al [1992 and 2001].

However, several authors have pointed out that de jure independence may not be a good

proxy for actual independence, particularly in a developing county context.18 Cukierman

et al [1992] therefore developed an alternative proxy for actual independence based on

the average turnover of central bank governors in a given period. A high turnover rate is

taken to indicate a low degree of central bank independence: if governors are easily

replaced then they are less likely pursue policies that are disadvantageous to the

government19. The proxy appears relevant in the context of time horizons of the

monetary authority: the higher the turnover rate, the less a governor will gain from

pursuing long-term policies, as he will be punished for imposing short-term costs20.

Using the period average of governor turnover rather than the year of governor change-

over limits the endogeneity problem arising from the potential of governors being

sacked as a political response to devaluation. The turnover rate variable was not

available for the 1990s and was therefore constructed as the number of central bank

governors divided by number of years the central bank / currency existed from 1990-

1999.21

Regarding the time horizons of politicians there are a number of potential proxies.

However, some of these proxies tend to be highly correlated with each other and are

therefore not used together in the regressions.22

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Degree of democracy: Democratic regimes are more accountable to the electorate than

autocratic regimes and face the risk of being replaced at elections (or through a break-up

of a ruling coalition) if unemployment rises and growth plummets. Democracies are

therefore likely to be sensitive to the short-term cost of peg defence and likely to

discount the long-term benefits of a stable currency more strongly than autocracies.23

On the other hand a large devaluation would undermine the government’s reputation for

competent economic management and democracies are likely to be more sensitive to

this than stable autocracies.

Concentration of political power: Among democracies the least stable form of

government under deteriorating economic conditions is a multi-party coalition, as under

economic pressure individual parties tend to withdraw from the coalition agreement for

fear of being associated with economic mismanagement. On the other extreme are

single-party governments with large majorities, as they are unlikely to be quickly

replaced in response to electoral discontent and can therefore more easily absorb short-

term economic costs.24

Veto-player changeover: This variable from the Beck et al [2001] database records the

proportion of political veto-players (president / government / second house), which are

replaced in a year. If the variable is used as an annual indicator of the extent of political

instability, there is a potential reverse causality problem, as a government is unlikely to

survive a catastrophic devaluation. It was therefore not used in the regressions studying

the probability of devaluation. If averaged over the period 1990 –2000 period (or

whichever sub-period the country existed) it proxies for the time horizon of politicians,

with high turnover rates indicating that politicians have little scope for maximizing

welfare over a long time horizon. However, the averaged variable was not significant in

any specification of the regressions and is therefore omitted from the reported results.

Veto player turnover can; however, be used in the regressions on devaluation size to

study the effect of political instability on the government’s ability to limit the size of

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devaluation.25 The variable is only significant in purely political models of devaluation

size, as soon as economic indicators are added to the model the statistical significance

of the variable disappears and the explanatory power of the model improves greatly. It

is therefore omitted from the reported results.

Election Year: A government is less likely to undertake politically costly defence of the

exchange rate and is more likely to use monetary and exchange rate policy to boost

employment when they face an election. The effect of an election year on the size of

devaluation on the other hand is ambiguous – before an election a large devaluation

would lower the probability of re-election. On the other hand after an election an

incoming government may choose to boost the economy by devaluing, particularly if

the new administration does not feel bound by the commitments made by its

predecessors. The variable takes the value one if there was a legislative and/or an

executive election in the year.26

Banking sector instability: According to banking sector models of currency crises, a

higher interest rate destabilizes weak banking systems, as weak debtors fall behind with

their payments and depositors start to withdraw in response to lower portfolio quality.27

Finding a proxy capturing the solidity of the banking system in developing countries is

difficult, as data about the proportions of bad loans are very limited (both in terms of

countries and years for which they are available) and often do not accurately reflect the

true extent of problem loans, as different countries have different regulations regarding

the declaration of bad loans.28 Therefore a number of broader measures of financial

fragility and financial development are currently used in the academic literature on

financial and currency crises.

Banking crisis dummy: The incidence of a banking crisis in an economy may be

endogenous to the occurrence of a currency crisis: banking and currency crises tend to

occur together.29 The direction of causality may run either way, as the currency crises

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could be caused by the weakness in the banking system. On the other hand a

devaluation may destabilize a banking sector which has borrowed in hard currency to

make loans in domestic currency. For this paper a dummy variable was used taking the

value 1 if there was a banking crisis starting or ongoing in the previous year, to avoid

the causality problem surrounding twin crises30. However, as the variable is never

statistically significant it is not reported in the results tables below.

Financial Depth: Broad money as a ratio of GDP is sometimes used to measure the

level of a country’s financial development. However, in financially underdeveloped

countries a large component of broad money is currency held outside the banking

system. Demetriades and Hussain [1996] suggest that any measure of financial

development should exclude currency in circulation from the broad money stock. In this

paper (M3–M1)/GDP from the World Development Indicators is used as a proxy for

financial development. Banking systems, which are more developed and perceived to be

more stable, will attract a larger amount of long-term deposits. Excluding sight deposits

also reduces potential endogeneity problems, as people withdraw short term deposits in

the face of an emerging “twin crisis”.

A number of alternative proxies were also considered, which might capture weaknesses

in the banking system. Liquid reserves / total assets in banking system: A high level of

reserves could be indicative of a financially repressed or unstable banking system in

which banks are increasing their cash positions in anticipation of bank runs31. On the

other hand, a high level of reserves may help to prevent liquidity problems in the

banking sector, so the effect of this variable is ambiguous and indeed it is not significant

in any of the regressions. Interest rate spread (lending minus deposit rate): This is

another measure of the efficiency and competitiveness of the banking system. However,

the interest rate spread also tends to be linked to inflation performance, so it is not a

pure indicator of banking sector performance and is therefore omitted from the

10

regressions. Bank Ratings: Default and operational risk ratings for banks would appear

to be a highly relevant measure of vulnerability to interest rate changes. However,

ratings have been shown to be highly pro-cyclical.32 For example in the Asian crisis

country and corporate ratings deteriorated markedly after the crisis had broken, making

this variable potentially endogenous. Standard and Poor’s information on financial

systems such as the “share of gross problematic assets” have only been available since

1998.

3 Preferences

These variables are based on the “optimal currency area”33, the “fear of floating”34 and

political economy arguments. The theory of optimal currency areas sets up a cost-

benefit analysis for a country based on how exposed its economy is to exchange rate

fluctuations and how costly it is to address trade deficits through internal adjustments

rather than changes in the exchange rate. There is a caveat, however, that the

characteristics of size of the economy, openness and the level of development can be

highly correlated.

GDP: larger economies tend to have some influence on the price of traded goods.35

Large countries are therefore not as exposed to international price shocks and therefore

have less to gain from fixing their exchange rates.

Openness is a measure of how exposed the economy is to fluctuations in the exchange

rate.36 In relatively closed economies exchange rate fluctuations only affect a few

internationally traded commodities, making pegging less attractive.

GDP/Capita: is sometimes used as a measure of diversification of the economy and

hence a measure of how exposed the economy is to foreign demand shocks. In

diversified economies disturbances in individual markets will offset each other. While a

diversified economy is less likely to peg, diversification is likely to make a float

relatively smooth. The main problem with this variable is its high correlation with other

11

explanatory variables, such as its correlation coefficient of 0.63 with the financial

development variable and a correlation coefficient of 0.47 with the openness variable.

The correlation coefficient between GDP and GDP per capita is 0.24. GDP per capita

is never significant in the regressions alongside the other indicators of preferences and

is therefore omitted from the reported regressions.

Terms of trade shocks: This variable captures diversification of trade as well as

pressures to devalue to restore competitiveness37. Countries with well-diversified

geographical and product group trade patterns are less likely to be affected by external

shocks. Lagged changes in import and export prices are used to avoid picking up effects

of price shocks linked to a devaluation. As an alternative a variable capturing the size of

the current account surplus / deficit is used in some regressions, which is discussed

below.

Fear of Floating: According to the fear of floating hypothesis countries there are

multiple reasons why countries prefer to suppress variation in their exchange rates.38

Countries with unhedged foreign currency denominated debt (pervasive in emerging

markets) have an incentive to peg to the currency in which they have borrowed. A

devaluation compromises the country’s ability to service its debt, as revenues are

generated in local currency.

Foreign currency denominated external debt/GDP39: It is argued that a high level of

foreign currency denominated debt should increase a government’s commitment to a

peg. On the other hand, a high level of foreign debt also makes a government more

vulnerable to changes in investor confidence. At times of crises the supply of external

funds becomes inelastic, risk premia rise and make it difficult to service the debt.40

Market forces may therefore be more important than the government’s preferences.

12

A second reason for governments to limit exchange rate fluctuations arises from a

combination of lack of credibility and the pass-through from exchange rates to prices,

which interferes with inflation targeting. A high degree of dollarisation of the economy

indicates such credibility problems: In countries with a history of high inflation and

frequent devaluations savers tend to hold hard currency deposits instead of deposits in

the local currency. If there are restrictions on hard currency deposits or the banking

system is fragile, asset substitution takes the form of “currency dollarisation”, where

people hold foreign cash in under the mattress savings instead of local currency deposits

or cash.41 This lowers the amount of domestic currency in circulation and exaggerates

the inflationary effects of expansionary monetary policies. Maintaining a stable

relationship between the domestic currency and the hard currency of choice in the

country is often hoped to reverse or at least prevent further dollarisation. However,

given the two aspects of dollarisation the more obvious “deposit dollarisation” and

“currency dollarisation”, which is more difficult to measure, there are no comprehensive

and comparable datasets of the degree of dollarisation in less developed countries and

this aspect of “fear of floating” is not examined statistically in this paper.42

In political economy the question of political preferences is discussed in addition to

preferences based on the economic structure.

Left-wing dummy: Left wing governments are seen as more focused on internal

(employment / growth) rather than external objectives.43 This variable was developed

for the OECD context and is based on words in the party name such as conservative /

socialist / labour. It may therefore not capture the political preferences of developing

countries, where the political spectrum is more likely to be split along ethnic or

nationalistic lines than a traditional left-right spectrum. The proxy takes the value 1 in

each year the Beck et al [2001] database records that a left-wing government was in

power.

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4 Conflict between pegs and domestic economic conditions

The first three variables are based on the first generation currency crisis literature, in

which crises are caused by a contradiction between the announced peg and the

government’s fiscal and monetary policies44. The fourth variable (economic growth) is

included according to second-generation macro-economic feedback models, in which

the government is concerned about domestic economic performance and abandons the

peg to concentrate on internal balance. All data are from the World Development

Indicators.

Fiscal Deficit: Large deficits (lagged) may indicate a need for seigniorage finance,

endangering the peg. However, as fiscal data are not widely available, this variable is

not used in the regressions, but (lagged) inflation is used directly.

Inflation: Countries whose inflation rates diverge from those of the anchor countries

will find it difficult to maintain currency stability over extended periods of time. Lagged

inflation is used, as current inflation will be affected by devaluations through rising

import prices.

Log of foreign exchange reserves: According to Krugmann’s [1976] model of currency

crises one of the leading indicators of currency crises is the loss of foreign exchange

reserves as domestic credit grows. The variable is lagged by one year to circumvent

endogeneity problems, as a speculative attack will deplete foreign exchange reserves.

Although the currency crises in the 1990s have shown that central bank reserves in

LDCs are not sufficient to avert a currency crisis in the face of a concerted speculative

attack, foreign exchange interventions may help to limit the size of a devaluation.

Recession: Lagged GDP growth reflects the government’s temptation to inflate the

economy to achieve internal balance (current GDP growth may be endogenous to a

devaluation occurring – see e.g. the recessions after the Tequila and Asian crises).

14

Current account imbalance: Large current account deficit (lagged) may indicate a need

to devalue to achieve external balance. The lagged variable is used, as the cost of

imports increases and export revenues decrease immediately after the devaluation event,

as part of the J-curve effect. The current account variable is used alternately with the

terms of trade shocks variable.

5 Liquidity of the foreign exchange market and central bank reserves

Variables capturing liquidity should be included to control for the magnitude of capital

outflows during a period of currency instability. The lower capital mobility the greater

the scope for using foreign exchange reserves rather than the interest rate to defend the

peg. There are a number of variables capturing the liquidity in a market. 45 High bid-

offer spreads may reflect explicit transaction costs such as taxes, inventory-carrying

costs and order-processing costs by dealers, as well as oligopolistic market structures.

High transactions costs lead to thin markets. The problem with using this measure is

that foreign exchange risk is part of the transaction costs implicit in bid-ask spreads

(through inventory-carrying costs). Turnover ratios and trading volumes measure the

breadth of a market. However, there are few data available for OTC markets like the

foreign exchange market. Market efficiency coefficients measure the liquidity of a

market by looking at how new information affects prices in the short term – how

smoothly and quickly do prices adjust to their equilibrium level? However, this measure

tends to deteriorate in advance of episodes of currency crises due to foreign exchange

market in interventions (damping effects) or inaccurate price determination due to

uncertainty regarding fundamentals (excessive volatility). As these traditional measures

of liquidity are either unavailable or inappropriate in the context of currency crises, a

crude proxy of market size might be more appropriate. Size of financial market (M3):

Relatively large and developed financial markets are likely to receive more foreign

speculator interest, than small and underdeveloped financial markets. However, this

15

variable is highly correlated with GDP (the correlation between logM3 and logGDP is

0.96) and is therefore omitted from the regressions.

6 Control variables

A further area of research in the field of currency crises is that of contagion, that is

financial instability spreading from one country to its trade partners or among countries

perceived to have similar characteristics.46

Year 1997 dummy: Of the various year-dummies introduced into regressions, the only

significant contagion effects are observed in the Asian crisis.

7 Descriptive Statistics

Insert table 1 here

The descriptive statistics presented in table 1 lend preliminary support to most of the

hypotheses explored above. Country years without devaluations have on average lower

central bank governor turnover than country years in which a devaluation is

experienced. The difference in the degree of democracy is even more pronounced: the

mean in the no devaluation cases is highly autocratic (-7.288 with a minimum of –10),

whereas the mean in the devaluation countries is democratic, even if not highly

democratic (1.851 with a maximum of +10). The difference in concentration of power

and election years is not as prominent, but the no devaluation cases have a lower

proportion of elections and a greater concentration of power as expected.

Of the variables capturing preferences no strong conclusions can be drawn from the

small differences in the size of the economy. However, the no devaluation cases are on

average more open economies. The “fear of floating” hypothesis is contradicted as the

no devaluation cases have a lower ratio of external debt to GDP than the devaluation

16

17

cases. The other economic variables presented show that the degree of vulnerability as

captured by the first generation currency crises variables (inflation and fiscal deficit) is

markedly lower in the no-devaluation cases than in the devaluation cases. The second

generation variables also receive some support as export prices in the no devaluation

cases were increasing faster and growth was higher on average than in the no

devaluation cases. However, there are no prominent differences in the levels of foreign

exchange reserves or the size of the current account deficits. Finally the year 1997 saw a

higher than average proportion of devaluations.

III: Methodology

The main focus of this paper is to examine the differences between the factors that

govern whether a currency remains stable or not and the factors that determine the size

of a devaluation if it does take place. The methodology chosen is to analyse the first

question (of peg stability) with a panel logit analysis, which examines what factors

affect the odds ratio of a devaluation event occurring.47 The regression takes the form:

(1) Ln [P /(1-P)] = β0 + β1 x1 + β2 x2 + β3x3 +… + σu + εit

P is the probability of getting outcome A and (1-P) is the probability of not getting

outcome A. The ratio P / (1-P) is the odds ratio and denotes the odds in favour of getting

outcome A. The probability of the devaluation occurring is modelled as a function of a

range of fixed effects from the independent x variables, plus a random effect (σu) plus

an error term (εit).

The dataset is in the form of a panel with observations for up to 98 countries for up to

11 years (1990 – 2000). Therefore the logit function used is a cross-sectional time series

logit. In the panel cross sectional effects dominate the time series effects as most of the

institutional indicators do not change over time in a given country (e.g. the

concentration of power variable tends to be stable over time and the central bank

independence indicator is calculated as an average of 10 years and has no in-group

variance). Similarly there is no in-group variation in the dependent variable in 25

countries, which either remain stable exchange rates throughout the period (e.g. Saudi

Arabia) or devalue every year (e.g. Turkey). Therefore a random effects logit is

estimated.

Different combinations of the variables discussed above are used as explanatory

variables. In the cases where different proxies capture similar institutions or are highly

correlated alternative proxies were used in different regressions. The first set of

regressions focuses on institutional and political factors only. The second set of

regressions compares the political model to alternative economic models. The third set

of regressions uses both political and economic explanatory factors for a full model.

The fourth set of regressions uses the same combinations of explanatory variables as in

the full model to examine what governs the size of devaluation in country-years where

the change in the annual average exchange rate exceeds 2%, using a linear regression.

IV: Results

1 Factors affecting the probability of devaluation

1.1 A Political Model

Insert table 2 here

The proxy for central bank independence is highly significant in all the

regressions looking at institutional variables only. Any increase in the turnover of

central bank governors (interpreted as a high degree of political interference in

monetary policy) strongly raises the probability of devaluation and the coefficient is

relatively robust in different specifications of the regressions.

18

The degree of democracy is also a highly significant explanatory variable. More

democratic countries have a raised probability of devaluation, suggesting that more

democratic governments find it more difficult to impose the cost of peg defence on their

populations. On the other hand it is possible that the degree of democracy here proxies

for other factors of development.

Regressions 1:3 and 1:4 provide support for the hypothesis that strong

governments (either due to an autocratic regime or a democratically elected government

which faces little effective opposition) can avoid devaluations, as costs of adjustment

can be imposed on the populations. Concentration of power has a negative and

significant effect on the probability of devaluation.

There is also support for the hypothesis that short time horizons raise the

probability of devaluation with election years being statistically significant explanatory

variables (regressions 1:1 to 1:4). Another explanation for the result would be that an

incoming government does not feel bound by the commitments regarding peg stability

made by a previous government. 48

Neither regression 1:2 nor regression 1:4 lends any support to the political

economy argument that left-wing governments tend to be more focused on internal

balance rather than exchange rate targets. While the hypothesis may be confirmed in a

developed country sample, in LDCs the party name (on which the variable is based)

does not appear to give much information on the government’s exchange rate regime

preferences or the perceived credibility of its commitment to the peg.

Regressions 1:3 and 1:4 suggest that more developed banking sectors are less

vulnerable to attacks. This means that the aspect of the variable, which captures the

stability of the financial system, dominates that of foreign speculator interest and

activity on a country’s foreign exchange market.

19

To compare the political model to its alternatives regression 1:5 uses the economic

factors that describe preferences instead of institutional variables. As expected larger

and more diversified economies are less likely to peg. Openness is not statistically

significant. The positive coefficient on the external debt variable contradicts the “fear of

floating” hypothesis – higher levels of debt make it more difficult to peg. Overall the

preferences model does not perform as well as the political / institutional model, despite

being estimated on a larger dataset.

Regressions 1:6 and 1:7 use economic fundamentals variables only. Regression 1:6 is

again estimated using a larger set of observations than the political model, but none of

the economic variables is statistically significant. Regression 1:7 includes fiscal

performance as an explanatory variable, which reduces the data-set to 699 observations.

In this reduced dataset inflation is significant at the 10 per cent level with the expected

sign. Neither economic model outperforms the institutional model.

1.2 The Full Model

Insert table 3 here

When political, institutional and economic factors are included in the regression

simultaneously, the main factor determining whether or not a country devalues still

appears to be the position of the central bank in the polity. Countries with central banks

whose governors are frequently replaced have a much raised probability of devaluation

(regressions 2:1 – 2:3). None of the other factors describing the political institutions

retains its explanatory power in the regressions including economic factors in regression

2:1 to 2:3, though the expected signs are retained, with democracy raising the

probability of devaluation while concentration of power lowers the probability of

devaluation.

20

The financial development variable, however, remains (mostly highly)

significant in lowering the probability of devaluation as was expected from the

institutional analysis: financial systems perceived as trustworthy destinations for long

term savings are more likely to withstand interest hikes in defence of currency pegs.

The significant positive coefficient of the GDP variable on the probability of

devaluation confirms the optimal currency area argument, that larger economies have

less interest in pegging the exchange rate. Another interpretation of the positive

coefficient on the GDP variable is that it partially proxies for foreign interest in the

country and hence the speculative pressures that can be brought to bear on a country’s

exchange rate peg.49 The OCA argument that more open economies would prefer

greater currency stability is not backed up by the results in table 2.50

The “fear of floating” argument that a high level of external debt to GDP should

predispose a government to maintaining the peg is not supported by the regressions and

regression 2:3 and 2:4 contradict it. The fact that a country has a large amount of

external debt appears to make it particularly vulnerable to reversals in investor

confidence and hence currency crises.

The final variable that is significant in all the regressions 2:1 to 2:4 is the year

1997 dummy, which shows that in terms of devaluations this year was indeed

exceptional (unlike any other year dummy). This lends support to the contagion

hypothesis.

None of the variables proxying for the extent of economic tensions and

governments’ temptation to reflate their economies has an effect on the probability of

devaluation. Lagged inflation is significant at the 10% level in regression 2:3, but the

effect is opposite to what would be expected, as higher rates of inflation appear to lower

the probability of devaluation. Lagged growth, the position of the current account and

lagged changes in export prices have no significant effect on the probability of

21

devaluation. Similarly the extent of foreign exchange reserves does not influence the

ability of a government to maintain a peg.

In regression 2:4 the central bank governor turnover proxy is omitted from the

regression to test whether governor turnover is a proxy of the government’s overall

policy preferences and hence economic outcomes. Omitting the variable produces a

very similar pattern of results, except that the Herfindahl index of concentration of

political power now becomes highly significant with the expected negative sign.

Financial development continues to lower and the GDP variable raises the probability of

devaluation and the coefficients are robust. External debt / GDP and contagion continue

to be significant risk factors regarding the probability of devaluation.

2: Factors affecting the magnitude of devaluations

Insert table 4 here

Unlike the question of whether or not a country devalues, the magnitude of

devaluation appears to be dominated by economic factors. Political factors appear to

have some effect on the size of the devaluation, but they are not robust across different

specifications of the regression. In regression 3:1 the index of the concentration of

power has a positive effect on the size of devaluation. This makes sense in that if

powerful governments have the option of postponing adjustment to pegs (a negative

effect on the probability of devaluation) then if a devaluation occurs it is more likely to

be sizeable and again the government is less likely to be punished for this. This is

backed up by the negative coefficient on the democracy variable in regression 3:3,

which shows that although more democratic governments are less likely to prioritise an

exchange rate peg over internal objectives (from table 2), they are also likely to be more

worried about allowing large devaluations for fear of being punished for

mismanagement. Lastly regression 3:4, which omits the central banking variable, shows

that election years have a positive effect on the size of devaluation. Either internal

22

balance receives priority over the exchange rate in the run-up to the election, or the

devaluation occurs after the election and its magnitude is increased by political

uncertainty and incoming governments breaking promises made by their predecessors.51

In contrast to the regressions reported in table 2, the proxy for financial

development now has a highly significant positive coefficient, indicating that more

developed financial systems are more vulnerable to capital outflows.

Similarly the larger the amount of external debt / GDP the more vulnerable a

country is to large devaluations, contrary to the fear of floating hypothesis. While “fear

of floating” would suggest that if a devaluation cannot be avoided the government

should do its best to limit its size, it appears that a more indebted government is less

able to do this.

All the regressions in table 4 confirm that the magnitude of devaluations when

they occur is a function of the economic tensions within the economy. The higher the

inflation rate the country experienced in the past year the higher the exchange rate

adjustment necessary in the current year. However, the better the growth performance of

the economy the smaller the devaluation. While the current account variable is not

statistically significant, the lagged change in export prices is statistically significant in

regression 3:3 where it has the expected negative sign – if export prices went up in the

previous year there is less need for a devaluation of the exchange rate.

Regressions 3:1 and 3:4 suggest that a high level of foreign exchange reserves

may help a country limit the size of a devaluation, even if it is ineffective in preventing

a devaluation. However the coefficient is only significant at the 10% level in two

regressions and is insignificant in the other regressions. Therefore the result is not

robust.

Finally, while contagion appeared to be a significant factor in explaining

whether or not a country devalued, it does not appear to have a statistically significant

effect on the magnitude of devaluations in 1997.

23

3: Discussion of Results

3.1 Robustness of results:

The logit regression results were tested for robustness, considering both the sensitivity

to outliers in the dependent variables and the definition of the dependent variable. The

results of the logit regressions are not materially altered in terms of significance levels if

the 9 countries, which had a single central bank governor from 1990 –1999, are

excluded from the regression. Similarly removing the four countries with more than 5

central bank governors in the period does not alter the results significantly. The results

for the democracy variable are sensitive to excluding the 7 highly autocratic countries

(with democracy scores of -10 and –9). If these countries are excluded the democracy

variable loses significance and the banking sector variable takes on significance at the

2% level instead. When the 19 countries with very high scores of democracy (9 or 10)

are excluded, the political variables remain significant: either at the 1% level (Central

bank and banking sector) or the 5% level (democracy, elections and the left-wing

government dummy) but the predictive power of the political model is reduced

somewhat (to 67.6%). Similarly the results are robust to excluding observations with

low concentration of power, but the Herfindahl index loses statistical significance when

the observations with a high concentration of power (115 observation where

concentration of power =1) are excluded from the analysis. Finally excluding the 92

observations of extremely low banking sector development does not alter the results

significantly, but the significance of the banking sector variable and the fit of the

regression are improved if the 84 observations of highly developed financial systems

(such as Hong Kong, Singapore, Malta and Cyprus) are excluded from the regressions.

24

The results reported for the logit analysis (whether or not a devaluation occurs) used a

devaluation greater than 2% as the cut-off point for the dependent variable. If the

dependent variable is reclassified using a devaluation of 5% as the threshold, the

election year dummy in the political model loses statistical significance, the significance

level of the democracy variable declines to 5%, the banking sector variable is significant

at the 5% level rather than the 1% level in regressions 1:1 and 1:2. The concentration of

power is only significant at the 10% level in all four regressions.

Regressions 1:1 to 1:4 and 2:1 to 2:3 show the central importance of the central bank’s

position in the polity. But it is also possible that there is an endogeneity problem: the

fact that the country devalues may cause the government to fire the central bank

governor. This problem is partially addressed by averaging turnover over the decade.

Moreover, regression 2:4 shows that the results obtained are not purely dependent on

the inclusion of the central banking variable, but that the explanatory power of the

model is maintained when the central bank variable is omitted. Instead the concentration

of power variable, the GDP variable and the external debt variable take on additional

significance and the inflation rate changes to the expected positive sign. This suggests

that there is a problem of multicollinearity in the data. However, the correlation

coefficients between governor turnover and the Herfindahl index is the highest at 0.25,

the lagged inflation rate 0.22, Log of GDP 0.16 and external debt 0.01.

3.2. Interpretation of Results:

The regression results reported in tables 2 and 3 show that the model correctly predicts

just over 70% of devaluation / stability observations. Interestingly the predictions of the

full model controlling for economic factors and the contagion effect in 1997 does not

perform significantly better than the “pure” institutional model. Indeed most of the

additional explanatory power arises from the 1997 dummy. Economic variables become

25

significant in the linear regression models of table 4, where variables capturing

economic pressures to devalue are highly and robustly significant.

Looking at specific country cases, the logit models correctly predict devaluation

probabilities in excess of 80% throughout the period for countries like Venezuela, on

account of its low financial development and its 4 central bank governors in the 1990s,

plus a relatively democratic and decentralized polity. On the other side of the spectrum,

the model correctly predicts extreme stability for all the Gulf States with their

autocratic, centralized polities and generally long-lived central bankers, as well as

relatively well-developed financial sectors. Pure institutional models predict devaluation

probabilities of about 25% for Saudi Arabia through the decade, while the mixed

models hover around 35% devaluation probability. For countries that have experienced

a process of democratization the models correctly predict increasing vulnerability. For

example the probability of devaluation in South Africa rises by 15 percentage points

between 1990 and 2000. However, for countries, which have a mixed pattern of

episodes of stability and years of devaluation, the models are often not sensitive enough

to predict year on year performance, except for instability associated with elections and

contagion in 1997.

For an imaginary country at the mean of the sample distribution – what are the effects of

changing the statistically significant explanatory variables?52 In the institutional model

of regression 1:2 a country at the mean of the sample distribution has a 70.2%

probability of devaluation. A hypothetical country with all the worst characteristics has

a near certainty of devaluation and a country with all the best characteristics has almost

no risk of devaluation (results presented in Table 5). If a country at the mean of the

sample distribution has just one additional central bank governor the probability of

devaluation rises by 11%. Similarly moving from the mildly democratic mean to a fully

26

democratic polity raises the probability of devaluation by 9.3%. Finally, election years

in a country at the mean raise the probability of devaluation by 15 percentage points.

For the full model including economic and control variables (regression 2.2) the

hypothetical country at the sample mean has a probability of devaluation of 72.3%. For

the mean observation of country-years with stable exchange rates the probability of

devaluation is 55.7%. This rises to 77.9% for the mean of the observations in which a

devaluation occurred. Holding all variables at the sample mean but adding an extra

central bank governor raises the probability of devaluation by 6.5 percentage points,

while contagion effects in 1997 increased the probability of devaluation by 10.3

percentage points. If a country at the sample mean increases its financial development

by one standard deviation (29.2%) then the probability of devaluation is reduced by

11.6% and a country at the maximum level of financial development (and all other

variables at the mean) has a probability of devaluation of just 7.6% (results presented in

Table 6).

V: Conclusions

Many developing countries continue to prefer “intermediate” exchange rate regimes to

the “corner solutions” of free floats or hard pegs supported by currency boards or

outright monetary unions even after the series of currency crises of the 1990s.53 If

countries continue to manage their exchange rate, policy advice on how to make such

regimes stable is important. This paper used LDC data to analyse the factors

determining the probability of devaluation and then compared them to the factors that

determine the magnitude of a devaluation in country-year observations where a

devaluation occurred. Potential explanatory variables were drawn from a review of the

literatures on regime choice and on currency crises.

27

It was shown that institutional factors play an important role in determining whether or

not a less developed country devalues. Of particular importance appears to be the

central bank’s time horizon. Under strong governments the probability of devaluation

seems to be lower. The strength of the financial sector also plays a role in determining

the probability of devaluation, with more developed financial sectors more able to cope

with the monetary implications of maintaining a peg. Further factors undermining the

ability to maintain a peg are the level of foreign debt and contagion factors. The degree

of economic imbalances in an economy, however, seems to have little explanatory

power when it comes to the maintenance of a stable exchange rate in a given year.

A different picture emerges from the analysis of the determinants of the size of

devaluation where economic pressures such as the inflation rate and the growth

performance of the economy dominate. While the coefficients of the institutional factors

maintain their expected signs, the coefficients are no longer robust to different

specifications of the regression, though there is limited evidence that highly

concentrated polities have larger devaluations and more democratic countries limit the

size of devaluations. Election years seem to produce greater devaluations than non-

election years. The only robust institutional variable is the financial sector variable.

However, in contrast to the logit analysis, greater levels of financial development have a

positive effect on the magnitude of devaluation, perhaps because more developed

systems facilitate greater capital outflows.

The regression results indicate that a devaluation is a two-stage process. The decision

whether or not to devalue is influenced mainly by the institutional set-up of the country,

which determines whether a pegged exchange rate arrangement appears to be credible.

Once a country decides to float, however, the size of the devaluation is mainly driven by

28

economic fundamentals, though there is some evidence to suggest that democratically

elected governments may try to limit devaluations. Future research should concentrate

on refining the proxies for the political, central banking and particularly the financial

fragility proxies. This would allow us to draw more specific conclusions for policy

advice to LDCs choosing to peg their currencies.

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Notes 1 E.g. Benassy-Quere and Coeure [2002]; Berg and Borensztein [2000]; Poirson [2001] 2 McKinnon [1963], Heller [1978], Dreyer [1978], Holden et al [1979]; Rizzo [1998] 3 Calvo and Reinhart [2000] 4 Edwards [1996] Meon and Rizzo [2002] 5 Krugmann [1979] 6 e.g. Obstfeld [1994], Ozkan and Sutherland [1995] 7 e.g. Alba et al [1998], Bisignano [1999] 8 e.g. Bussiere and Mulder [1999]; Leblang and Bernhard [1999] 9 e.g. Bussiere and Mulder [1999]; Leblang and Bernhard [1999] 10 e.g. Meon and Rizzo [2002], Poirson [2001] and Benassy-Quere and Coeure [2002] 11 e.g. Benassy-Quere and Coeure [2002] use 1980s governor turnover to examine regime choice in the 1990s 12 e.g. Bussiere and Mulder [1999] and Leblang and Bernhard [1999] 13 IMF Exchange arrangement and exchange restrictions 14 This categorisation differs from the dependent variables in studies examining peg choice, which look at the choice between hard pegs, soft pegs and pure floating, based on either declared or de facto regime choices. (See for example discussion on “classification of exchange rate regimes” in Poirson [2001] pp 6-12 and Bubula and Otker-Robe [2002] pp 6-13) The advantage of the proposed categorisation is that it offers an unambiguous way of dividing the sample for the two stages of the analysis. 15 Drazen and Masson [1994]; Ozkan and Sutherland [1995]; Obstfeld [1994] 16 This idea has long been part of the literature on central banking and inflation, but has not been empirically tested in the context of currency crises. 17 Grilli et al [1994] 18 eg Cukierman [1992]; Forder [1996] 19 However, there is an alternative argument that a government would not replace a very compliant central banker, whereas an independent-minded governor could lose his job. In this case low turnover would indicate a low degree of independence. In practice governments tend to blame compliant central bankers for negative policy outcomes and sack them as scapegoats – see for example the 6 central bank governors in Brazil’s high inflation episode from 1990 – 1994. 20 An alternative interpretation of this proxy would be that rather than capturing an underlying power relationship between the government and the central bank it captures the degree of monetary policy stability, especially in autocracies where there may not be a way of implementing meaningful central bank independence / separation of powers. 21 Based on the methodology suggested by De Haan and Kooi [2000] using IFS yearbooks and central bank web-pages. Ranges from 0.1 to 0.9.

33

22 The correlation coefficient between the concentration power and the degree of democracy is -0.51 (autocracies tend to have high concentration of power) and democracies also have higher average instability (correlation coefficient of 0.42) 23 The variable ranges from –10 (completely autocratic) to +10 (completely democratic) with a mean of about 1. From Polity IV database. 24 The proxy is based on the Herfindahl index collected in the Polity IV database. The value of the index H, is the sum of the squares of the market shares of all parties in a polity, giving the highest scores to single-party polities. The proxy is not particularly useful for discriminating between stable and unstable autocracies as an unstable democracy may either be characterised by rising opposition representation or by a complete clampdown on political competitors. 25 E.g. in Indonesia in 1997 / 98 political instability during and after the crisis created uncertainty about future policies and resulted in a much larger depreciation of the currency and a longer period of overshooting than in other countries affected by the Asian crisis, which maintained political stability, such as Malaysia and Singapore. 26 From Polity IV database. 27 Alba et al [1998], Bisignano [1999], Goldstein et al [2000] 28 Barth et al database of financial Institutions 29 e.g. Kaminsky and Reinhart [1998] 30From Caprio and Klingebiel [2003] In countries where it is not clear when the banking crisis ended (eg.1987-?) I assume it lasted five years from the start date. If the entry is “1990s” the variable takes the value 1 from 1991 – 2000. If the entry is “early 1990s” the variable takes the value 1 from 1991 – 1996. 31 From WDI 32 e.g. Ferri et al [1999], Monfort and Mulder [2000] 33 Heller [1978], Dreyer [1978], Holden et al [1979]; Rizzo [1998] 34 Calvo and Reinhart [2000] 35 GDP from WDI 36 Imports + Exports/ GDP from WDI 37 A series of export prices was constructed from the WDI series of export revenues in current local currency / export revenues in constant local currency and converted to US$ prices. Likewise for imports. 38 Calvo and Reinhart [2002] 39 WDI 40 This argument is presented by the liquidity crises models of currency crises in which the rise in the interest rate compromises a country’s ability to service its debt, e.g. Goldfaijn and Valdes[1996]; Radelet and Sachs [1998] 41 Ooomes [2003], Fielding and Shortland [2002] 42 Poirson [2001] uses foreign currency deposits / broad money in her study of exchange rate regime choice, however, these data are only available for a very small subset of the developing countries examined in this paper. 43 e.g. Simmons [1994] 44 Krugmann [1979], Fielding and Mizen [2001] 45 Sarr, A and Lybeck, T [2002] 46 Alba et al [1998]; Eichengreen et al [1996], Masson [1999] 47 Very similar results are obtained when using the probabilistic rather than logistic regressions. 48 This is consistent with the results of Bussiere and Mulder [1999] who find that both pre and post-election periods are times of particular vulnerability for exchange rate stability. 49 Based on the high correlation with the size of the financial market variable M3 discussed above. 50 Different transformations of the openness variable such as log openness and log (1+openness) were used alternatively to address the somewhat skewed distribution, but the variable was never significant. 51 Bussiere and Mulde [1999] who studied pre and post-election periods separately reach the conclusion that the post-election period is the most vulnerable period for currency pegs. 52 Choosing the regressions with the highest explanatory power for the institutional model (1:2) and the full model (2:2) 53 Benassy-Quere and Coeure [2002], Bubula and Otker-Robe [2002], Calvo and Reinhart [2002]

34

Table I: Descriptive Statistics: Mean values

Variable Sample No Devaluation

Devaluation >2%

Central Bank governor turnover 0.296 0.252

0.322

Democracy 0.927 -7.288

1.851

Concentration of Power 0.513 0.557

0.487

Election Year 0.241 0.198

0.264

(M3 – M1) / GDP 29.66 39.918

23.822

Log GDP 22.604 22.393

22.637

Openness (%) 82.19 97.20

74.63

External Debt / GDP 83.28 70.16

89.77

Lagged Inflation (%) 118.79 34.37 167.51 Lagged growth (%) 2.72 3.60

2.22

Lagged current account / GDP (%) -8.59 -8.09 -8.88 Lagged Fiscal deficit -2.82 -2.28 -3.16 Lagged change in exports prices (%)

2.03 3.852 1.07

Lagged log of foreign exchange reserves

5.883 5.972 5.833

Year 1997 0.09 0.06 0.11

35

Table II: Political / Institutional Model

Regression number 1:1 1:2 1:3 1:4 Central Bank Governor turnover

6.291***(1.902)

5.917***(1.739)

5.359*** (1.576)

5.215*** (1.555)

Democracy 0.056***(0.023)

0.055***(0.023)

Concentration of Power -1.037** (0.510)

-1.107** (0.501)

Leftdummy 0.390 (0.300)

0.272 (0.298)

Election Year 0.517** (0.219)

0.512** (0.219)

0.393* (0.214)

0.388* (0.213)

(M3 – M1) / GDP -0.008 (0.007)

-0.009 (0.006)

-0.018***(0.006)

-0.017*** (0.006)

Constant -0.948 -0.907 -0.281 -0.272 Number of Observations 934 934 882 882 % correctly predicted 70.9 71.7 71.0 71.5

Preferences Model Regression number 1:5 GDP / Capita -0.0002**

(0.0001) Openness -0.001

(0.003) Log GDP 0.187**

(0.083) External Debt / GDP 0.334**

(0.168) Constant -3.047

(4.957) # of Observations 1234 % correctly predicted

67.9% 54

*, ** and *** represent significance at 10%, 5% and 1% respectively Standard errors in parentheses

Imbalances Model

Regression number 1:6 1:7 Lagged Inflation 0.052

(0.071) 0.23* (0.12)

Lagged Growth -0.02 (0.013)

0.03 (0.026)

Lagged Current Account

-0.009 (0.008)

0.007 (0.012)

Forex Reserves 0.002 (0.06)

-0.038 (0.087)

Fiscal Surplus -0.087 Constant 0.761

(0.461) 0.273 (0.737)

# of Observations 1174 699 % correctly predicted

68.8%55

68.9%

54 Estimated on full set of observations but predictions restricted to same dataset as institutional model 55 as before

36

Table III: Factors Affecting the Probability of Devaluation – controlling for economic

conditions Regression number 2:1 2:2 2:3 2:4 Central bank Governor turnover

3.706*** (1.467)

3.567**(1.561)

2.995** (1.487)

Democracy 0.026 (0.027)

Concentration of Power -0.598 (0.526)

-0.658 (0.580)

-0.968*** (0.449)

Election Year 0.172 (0.242)

0.305 (0.246)

0.239 (0.254)

0.180 (0.219)

(M3 – M1) / GDP -0.024***(0.008)

-0.018* (0.010)

-0.026***(0.008)

-0.022*** (0.007)

Log GDP 0.207* (0.124)

0.141 (0.133)

0.221* (0.126)

0.213** (0.104)

Openness -0.001 (0.005)

-0.005 (0.005)

-0.003 (0.005)

-0.004 (0.004)

External Debt / GDP 0.435 (0.303)

0.435 (0.314)

0.599* (0.323)

0.548** (0.245)

Lagged log of inflation -0.061 (0.096)

-0.026 (0.101)

-0.194* (0.116)

0.015 (0.086)

Lagged growth -0.013 (0.015)

-0.019 (0.016)

-0.003 (0.017)

-0.013 (0.014)

Lagged current account / GDP 0.004 (0.013)

0.003 (0.014)

0.004 (0.010)

Lagged change in export prices 0.001 (0.007)

Lagged log of foreign exchange reserves

-0.098 (0.078)

-0.084 (0.082)

-0.080 (0.081)

-0.070 (0.064)

Year 1997 0.672* (0.365)

0.659* (0.359)

0.665*** (0.375)

0.961*** (0.331)

Constant -3.076 -2.308 -3.218 -2.602 Number of Observations 640 686 583 799 % correctly predicted 73.1 73.8 71.7 73.3 *, ** and *** represent significance at 10%, 5% and 1% respectively

37

Table IV:

Factors Determining the Size of Devaluation Regression number 3:1 3:2 3:3 3:4 Central Bank Governor turnover

-0.039 (0.201)

-0.000 (0.197)

-0.005 (0.178)

Democracy -0.006 (0.004)

-0.008** (0.003)

Concentration of Power 0.151* (0.092)

0.115 (0.077)

Election Year 0.052 (0.039)

0.025 (0.041)

0.047 (0.034)

0.073** (0.037)

(M3 – M1) / GDP 0.006*** (0.001)

0.006*** (0.002)

0.004***(0.001)

0.005*** (0.001)

Log GDP 0.016 (0.020)

0.016 (0.019)

0.023 (0.018)

0.013 (0.016)

Openness -0.0002 (0.0008)

-0.000 (0.001)

-0.000 (0.007)

-0.001 (0.001)

External Debt / GDP 0.203*** (0.032)

0.150*** (0.032)

0.197***(0.028)

0.134*** (0.027)

Lagged log of inflation 0.123*** (0.015)

0.147*** (0.016)

0.121***(0.015)

0.149*** (0.013)

Lagged growth -0.011***(0.003)

-0.008***(0.003)

-0.001 (0.003)

-0.011*** (0.003)

Lagged current account / GDP 0.0008 (0.002)

-0.001 (0.002)

0.000 (0.002)

Lagged change in export prices -0.002* (0.001)

Lagged log of foreign exchange reserves

-0.023* (0.013)

-0.020 (0.013)

-0.005 (0.012)

-0.016* (0.010)

Year 1997 0.025 (0.053)

0.001 (0.0546)

0.004 (0.046)

0.021 (0.048)

Constant -0.618 -0.649 -0.802 -0.561 Overall R-Squared 0.3517 0.3157 0.3072 0.354 Number of Observations 447 441 441 538 *, ** and *** represent significance at 10%, 5% and 1% respectively

38

39

Table V: Marginal Effects: Institutional Model

Co-

efficients At mean

Worst Best Extra Governor

Maximum Democracy

Election Year

Central Bank Independence

5.917***

1.751 5.3253 0.032 2.343 1.751 1.751

Democracy 0.055***

0.050 0.55 -18.51 0.050 0.55 0.050

Election Year 0.512**

0.123 0.512 0 0.123 0.123 0.512

(M3 – M1) / GDP

-0.009

-0.266 -0.006 -1.998 -0.266 -0.266 -0.266

Leftdummy 0.390

0.105 0.39 0 0.105 0.105 0.105

Constant -0.907

-0.907 -0.907 -0.907 -0.907 -0.907 -0.907

Xβ

0.85 5.86 -21.38 1.449 1.356 1.745

Probability 0.702 0.997 0 0.81 0.795 0.851

40

Table VI: Marginal effects: Full Model

Regression number

Coefficients At mean

Stable sample

Devaluers Extra Governor

Year 1997

Extra Fin. Developmt.

Central Bank Independence

3.567** (1.561)

1.056 0.899 1.149 1.413 1.056 1.056

Democracy 0.026 (0.027)

0.024 -0.189 0.048 0.024 0.024 0.024

Election Year 0.305 (0.246)

0.073 0.06 0.081 0.073 0.073 0.073

(M3 – M1) / GDP

-0.018* (0.010)

-0.534 -0.719 -0.429 -0.534 -0.534 -1.059

Log GDP 0.141 (0.133)

3.187 3.157 3.191 3.187 3.187 3.187

Openness -0.005 (0.005)

-0.411 -0.486 -0.373 -0.411 -0.411 -0.411

External Debt / GDP

0.435 (0.314)

0.361 0.305 0.391 0.361 0.361 0.361

Lagged log of inflation

-0.026 (0.101)

-0.066 -0.052 -0.073 -0.066 -0.066 -0.066

Lagged growth -0.019 (0.016)

0.035 0.047 0.029 0.035 0.035 0.035

Lagged current account / GDP

0.003 (0.014)

-0.026 -0.024 -0.027 -0.026 -0.026 -0.026

Lagged log of foreign exchange reserves

-0.084 (0.082)

-0.494 -0.502 -0.490 -0.494 -0.494 -0.494

Year 1997 0.659* (0.359)

0.059 0.039 0.072 0.059 0.659 0.059

Constant -2.308 -2.308 -2.308 -2.308 -2.308 -2.308 -2.308 Xβ 0.959 0.228 1.261 1.316 1.558 0.434 Probability 0.723 0.557 0.779 0.788 0.826 0.607